# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE140161"

# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE140161"

# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE140161.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE140161.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE140161.csv"
json_path = "./output/preprocess/3/Rheumatoid_Arthritis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Availability
# Yes - Series_overall_design indicates Affymetrix chip was used for whole blood transcriptome
is_gene_available = True 

# 2.1 Data Availability
# Disease state is constant "Sjögren's syndrome", not usable
trait_row = None  

# Gender is available in row 1
gender_row = 1

# Age is not available
age_row = None

# 2.2 Data Type Conversion
def convert_trait(x):
    # Not used since trait_row is None
    return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
    if value == 'female':
        return 0 
    elif value == 'male':
        return 1
    return None

def convert_age(x):
    # Not used since age_row is None
    return None

# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Clinical Feature Extraction skipped since trait_row is None
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract gene IDs and gene symbols from annotation data 
def get_gene_name(text):
    """Extract gene symbol from RefSeq annotation text"""
    if not isinstance(text, str):
        return None
    # Look for gene symbols after RefSeq
    match = re.search(r'RefSeq // Homo sapiens .+?\(([A-Z0-9]+)\)', text)
    if match:
        return match.group(1)
    # Also try looking for gene symbols after HGNC Symbol tag    
    match = re.search(r'\[Source:HGNC Symbol;Acc:HGNC:\d+\] // ([A-Z0-9]+)', text)
    if match:
        return match.group(1)
    return None

# Create mapping dataframe
mapping_data = pd.DataFrame({
    'ID': gene_metadata['ID'],
    'Gene': gene_metadata['SPOT_ID.1'].apply(get_gene_name)
})

# Map probes to genes and combine expression values
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview result
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# Save normalized gene data for future use
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Create minimal clinical features with constant trait
clinical_features = pd.DataFrame({'Sjogrens': 1}, index=gene_data.columns) 

# Link data and check bias
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
linked_data = handle_missing_values(linked_data, 'Sjogrens')
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Sjogrens')

# Validate and save info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True, 
    is_biased=trait_biased,
    df=linked_data,
    note="Dataset contains gene expression data but all samples are Sjögren's syndrome cases."
)

# Save if usable (won't be in this case due to constant trait)
if is_usable:
    linked_data.to_csv(out_data_file)